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Hi, I have taken another approach for using nd window over several dimensions of xarray objects to perform filtering and tapering, based on scipy.ndimage, scipy.signal and dask.map_overlap. @shoyer @jhamman it is somewhat similar to what I have presented during the aospy meeting. It also refers to the issue #819.

For the moment, I have something that works like this : ``` shape = (50, 30, 40) dims = ('x', 'y', 'z') dummy_array = xr.DataArray(np.random.random(shape), dims=dims)

Define and set a window object

w = dummy_array.window w.set(n={'x':24, 'y':24}, cutoff={'x':0.01, 'y':0.01}, window='hanning') `` where nis the filter order (i.e. the size),cutoffis the cutoff frequency,windowis any window name that can be found in thescipy.signal.windows` collection.

Then the filtering can be perform using the w.convolve() method, which build a dask graph for the convolution product.

I also want to add a tapering method 'w.taper()' which would be useful for spectral analysis. For multi-tapering, it should also generate an object with an additional dimension corresponding to the number of windows. To do that, I first need to handle the window building using dask.

Let me know if you are interesting in this approach. For the moment, I have planned to upload a github project for signal processing tools in the framework of pangeo-data. It sould be online by the end of December and I will happy to have feedback on it. I am not sure it falls into the xarray framework and it may need a dedicated project, but I might be wrong.

Sorry for not replying sooner. So far it works fine for me when I switch to pandas, use their gaussian rolling window, and then switch back to xarray. As I'm in a hurry with something else now, I will get back to this discussion a bit later.